Federated learning (FL) is a decentralized model for training data distributed across client devices. Coded computing (CC) is a method for mitigating straggling workers in a centralized computing network, by using erasure-coding techniques. In this work we propose approximating the inverse of a data matrix, where the data is generated by clients; similar to the FL paradigm, while also being resilient to stragglers. To do so, we propose a CC method based on gradient coding. We modify this method so that the coordinator does not need to have access to the local data, the network we consider is not centralized, and the communications which take place are secure against potential eavesdroppers.

翻译：联邦学习（FL）是一种用于训练分布在客户端设备上数据的去中心化模型。编码计算（CC）是一种通过使用纠删码技术来缓解集中式计算网络中掉队节点的方法。本研究提出在客户端生成数据的场景下近似求解数据矩阵的逆矩阵——类似于联邦学习范式，同时具备对掉队节点的容错能力。为此，我们提出一种基于梯度编码的编码计算方法。我们对该方法进行改进，使得协调器无需访问本地数据，所考虑的网络并非集中式架构，且通信过程能够抵御潜在窃听者的攻击。